Protein Prediction II for Bioinformaticians
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of WS 2011/2
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
|available module versions|
|SS 2015||WS 2011/2|
IN2230 is a semester module in English language at Master’s level which is offered in winter semester.
This Module is included in the following catalogues within the study programs in physics.
- Catalogue of non-physics elective courses
|Total workload||Contact hours||Credits (ECTS)|
|240 h||90 h||8 CP|
Content, Learning Outcome and Preconditions
Predicting protein function using sequence: sequence alignments, multiple sequence alignments, motifs, domain assignment, annotation transfer by homology, de novo predictions. Predicting protein function using structure: structural alignments, structural motifs, annotation transfer via structure similarity. From structure prediction to function prediction: comparative modeling; prediction of: subcellular localization, protein-protein interactions, protein-DNA and -RNA interactions, protein-substrate interactions, protein networks, Gene Ontology (GO), Enzyme Classification, prediction of enzymatic activity, prediction of functional classes (e.g. GO classes).
Prediction of the effect of single point mutations (sequence variants) on protein function and the organism. Prediction of phenotype from genotype.
As for the first part (Protein Prediction I), the module include an introduction into machine learning with particular focus on how to avoid over-estimating performance.
As opposed to the first part (Protein Prediction I), protein structure has only played a minor role: it has been introduced if it has been helpful to further our understanding of function.
Students can develop their own prediction methods (in groups guided by tutors) by combining existing methods, or algorithms, and / or create new methods.
The participants are able to analyze and to evaluate published methods (as readers of the publication, as peer-reviewers, and as competitors). Based on the outcome of these evaluations they are able to create a tool that is readily usable by experimental and computational biologists. This means, they can convert an abstract idea of a solution under consideration of technical aspects into pseudo-code and optionally further into executable programs during the exercises.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VI||6||Protein Prediction II for Bioinformaticians (IN2230)||Erckert, K. Olenyi, T. Rost, B. Senoner, T.||
Thu, 12:30–14:00, MI 00.13.009A
Mon, 10:00–12:00, MI 00.08.038
Tue, 12:30–14:00, MI 00.13.009A
Learning and Teaching Methods
Each team will thoroughly estimate the performance of the tool they created and the team will present their results to their peers and to the tutors.
Description of exams and course work
In the exam the participants demonstrate their ability to devise and discuss an appropriate computational approach for a solution for a biological problem in the area of function prediction. For example they choose the appropriate methods depending on the type of data they have (sequence data, annotation data, a.s.f.) as well as they can choose the appropriate data abstraction level (GO level, EC classes, a.s.f.) depending to the respective biological question.
They demonstrate their understanding of the concepts in the choice of appropriate solution approaches to the given tasks and they can evaluate these in terms of a discussion of the various pro's and con's of alternative approaches in biological as well as in technical aspects. They can demonstrate their ability to create a usable tool implementing a solution approach down to the level of pseudo-code.
Details are announced at the beginning of the module.